Homework is lacking. It is too easy to pass. I feel like the programming task or homework task fell short. The lectures were good but too little practice.

Good non-technical materials, but short enough to be incorporated into other courses. Some aspects feel subjective. Many typos/minor mistakes in quizzes

Was really enthousiastic about the first two courses in the specialization, the third however felt a bit like going back a step in level of advancement.

It is a good course and gives a lot of good advice. However, it would be good if there was a practical project to practice everything that was taught.

A different course for only two weeks of content? This is nuts. I waited for 15 days for financial aid to be approved and I completed it within 5 days.

Only interesting if you don't have much experience with machine learning; Might or might not be great if you are a novice, though - hard to say for me.

There was some very valuable material. However, I think some of the videos could have been prepared a little bit better and could do with more editing

Course was less interesting than the other parts. Also very negative that the last part of the 5-part specialization is taking ages to be finalized.

Good content, but could definitely benefit from a more fleshed out problem to solve. The content beg for a larger concrete coding exercice project.

Appears to be some errors in the section titles (Flight Simulator??). Also, some parts didn't seem to be as polished as the previous two courses.

It's easy and more simple than the others in specialization. Can be more deeper into ML project organization management. It's ok, could be better!

This course did not contain programming assignments, only quizzes, and was thus considerably less useful, even though the knowledge was important.

Quite some questions are confusing and some are not correct itself. and this course is more concept based, didn't actually get to program a lot.

Contains some useful tips, but they are a bit too diluted - I feel like it could have lasted much less and still conveyed the same information.

This course provides some interesting insights into how to approach machine learning projects, but feels a little light on substance at times.

Great content. Some strange audio that I think should've been cut (especially in week 2). I suspect the week 2 quiz is a little buggy as well.

The course material can be clubbed with existing courses. It would have been much more meaningful with some examples and hands-on assignments

Covered important topics and real-world project considerations. However, the content and assignments are too short to make it a full course.

This time it was not that well-structured than the previous courses. I thought we would learn how to structure step by step an ML project.

Lots of information, few knowledge

Change name to "Struc. Deep Learning Projects", all other forms of ML not considered, specially for P2.

Too much similar material to the prior course, and only two simple quizzes, no hands-on programming assignments like in earlier courses.

A fundamentally very good course with a few technical gltiches that can be easily corrected and some confusing elements to be clarified.

I think this lecture is important for every research scientist. However, there was no programming examples so I was confused sometimes.

No programming assignments or labs, so too much theory, and too little chance to put same into practice. Not a good value for my money.